Adaptive learning for efficient driving in urban public transport

Pozueco, Laura; Tuero, Alejandro G.; Pañeda, Xabiel G.; Melendi, David; Garcı́a, Roberto; Paneda, Alejandro G.; Rionda, Abel; Díaz, Gabriel; Mitre, Maria Augusta da Silveira

doi:10.1109/cits.2015.7297724

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…They model dangerous situations such as aggressive driving style, failure to maintain a safe distance, and a lack of ability to predict traffic flow. They were designed by a mechanical engineer who is an expert in driving behavior, and they are based on previous work [38,39]. They are used to evaluate driving and analyze if there is a relationship between the characteristics of the drivers, their driving behavior, and their stress levels.…”

Section: Driving Behaviormentioning

confidence: 99%

Beside and Behind the Wheel: Factors that Influence Driving Stress and Driving Behavior

et al. 2021

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A large percentage of traffic accidents are due to human errors. Driving behavior and driving stress influence the probability of making these mistakes. Both are influenced by multiple factors, among which might be elements such as age, gender, sleeping hours, or working hours. The objective of this paper is to study, in a real scenario and without forcing the driver’s state, the relationship between driving behavior, driving stress, and these elements. Furthermore, we aim to provide guidelines to improve driving assistants. In this study, we used 1050 driving samples obtained from 35 volunteers. The driving samples correspond to regular commutes from home to the workplace. ANOVA and ANCOVA tests were carried out to check if there are significant differences in the four factors analyzed. Although the results show that driving behavior and driving stress are affected by gender, age, and sleeping hours, the most critical variable is working hours. Drivers with long working days suffer significantly more driving stress compared to other drivers, with the corresponding effect on their driving style. These drivers were the worst at maintaining the safety distance.

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Section: Driving Behaviormentioning

confidence: 99%

Beside and Behind the Wheel: Factors that Influence Driving Stress and Driving Behavior

et al. 2021

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“…Efficiency is represented by the "Inertia" pattern, while the other three show inefficient habits, which we call "Acceleration-Brake", "Brake-Acceleration" and "Idling". We made a brief introduction to the efficiency and inefficiency patterns as a base of an adaptive learning system in [25]. The described patterns complement the learning evaluation process in conjunction with the analytic system.…”

Section: B Efficiency and Inefficiency Patternsmentioning

confidence: 99%

Analytic System to Evaluate Efficient Driving Programs in Professional Fleets

Pozueco

Tuero

Paneda³

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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Interest in energy cost saving and in global warming have persuaded transport companies to apply measures to reduce fuel consumption. Efficient driving is one of the most employed solutions as it allows savings in fuel consumption of around 10% with a minimal investment. The drawback is that efficient driving is a learning process and it greatly depends on the drivers' behavior, which in turn is closely related to their motivation. If drivers are not really involved or after some time their interest decreases, efficiency improvements would disappear. Thus, an efficient driving program should make drivers' motivation one of the main targets. One option could be the implementation of reward programs. However, these should be based on a clear individual evaluation process, as an unfair system could lead to discomfort, complaints and repudiation. In this paper, we propose an analytic system, based on the detection of efficient and inefficient behavioral patterns, to evaluate the individual driver's progression in efficient driving with the aim of being the basis of a reward program. The system receives relevant, driving related, vehicle information every 1.5 seconds, allowing a precise searching of patterns. It has been tested successfully in 16 bus companies, analyzing the performance of 880 professional drivers. To accurately illustrate the analytic process, three detailed driver analyses have been included as a case study. Results of this applied research on the eco driving field show that the proposed system identifies efficient and inefficient actions that are used to fairly evaluate the drivers' performance.

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“…This means that x3 and x4 can only take exact values. In this case x3 = [2, 3,4,5,6] and x4 = [0, 1]. The reason why there is no x3 = 1 term is that, in decelerations, the ECU (Engine Control Unit) always engages the first gear in idle conditions, so fuel consumption is never null.…”

Section: Linear Regression Modelmentioning

confidence: 99%

“…This method allows us to perform an objective evaluation of the skills of the drivers because it is based on real driving data. Furthermore, it is also able to facilitate adaptive training as we can see the evolution of the performance of the driver in each pattern and focus the reinforcement sessions on his or her mistakes [6]. All the real data of our studies are obtained by a system called Cated Box, which receives information directly from the vehicle's engine control unit (ECU) [7].…”

Section: Introductionmentioning

confidence: 99%

Economic Impact of the Use of Inertia in an Urban Bus Company

et al. 2017

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Public transport companies face great expenses. In order to be profitable companies, they need to reduce costs in all aspects. In addition, many countries have increased taxes to force transport companies to reduce their fuel consumption and, thus, greenhouse effect emissions. Efficient driving is the cheapest way to achieve this goal. In this paper, we analyze the economic impact of one of the most influential efficient driving techniques in an urban bus company. We calculate the difference of consumption between a well-performed deceleration using the inertia of the vehicle and a loss of speed obtained by using the brakes. For this purpose, we compare a real track performed by any driver in a precise vehicle with a simulated ideal track. To obtain the deceleration of the simulation, we develop a linear regression model on over 170,000 samples captured from the same vehicle over a period of one month. The results show that the costs of the vehicle under test in one month may be reduced by more than 2500 € by using this technique. With these results, we can see that it is worth trying to anticipate different situations while driving only by applying inertia adequately.

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Adaptive learning for efficient driving in urban public transport

Cited by 8 publications

References 8 publications

Beside and Behind the Wheel: Factors that Influence Driving Stress and Driving Behavior

Beside and Behind the Wheel: Factors that Influence Driving Stress and Driving Behavior

Analytic System to Evaluate Efficient Driving Programs in Professional Fleets

Economic Impact of the Use of Inertia in an Urban Bus Company

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